结合多特征和模糊偏好关系的高分辨率遥感影像分割

高空间分辨率(简称高分辨率)遥感影像除光谱特征外,还包含丰富的纹理特征,为了实现高分辨率遥感影像的高精度分割,提出结合多特征和模糊偏好关系的分割方法.首先,通过像素光谱测度定义多种统计特征,根据定义的各个特征提取特征影像并分别实现影像分割,利用其结果构建模糊决策矩阵;然后,基于像素定义特征间的模糊偏好关系矩阵,计算不同特征对最终分割决策的权重,并对模糊决策矩阵加权以突出优势特征,抑制劣势特征;最后,通过反模糊化决策矩阵得到最优影像分割结果.对合成影像和真实高分辨率遥感影像的分割结果进行定性和定量评价,结果表明,合成影像的分割总精度为99.8%,Kappa值为0.998,说明所提出的算法通过结合各特征的优势部分能够获得高精度的分割结果.

Combining multi-feature and fuzzy preference relation for high resolution remote sensing image segmentation

In addition to spectral features, the high spatial resolution (abbreviated as high resolution) remote sensing images contain rich texture features. In order to achieve high precision segmentation for high resolution remote sensing images, this paper proposes a segmentation method which combines multi-feature and fuzzy preference relation. Firstly, statistical features are defined by using pixel spectral measure. The feature image segmentation is realized according to the defined features and the segmentation results are used to construct the fuzzy decision matrix. Then, the fuzzy preference relation matrix is defined based on the pixel to calculate different features weight, and the fuzzy decision matrix is weighted to highlight the superior features and suppress the inferior features. Finally, the optimal image segmentation results are obtained by the defuzzification decision matrix. The qualitative and quantitative evaluation of the synthetic and real high resolution remote sensing image segmentation results show that the total accuracy of synthetic image is 99.8%, Kappa value is 0.998. The proposed algorithm combines the advantages of each feature, which obtains highly accurate segmentation results.